{smcl}
{com}{sf}{ul off}{txt}{.-}
      name:  {res}<unnamed>
       {txt}log:  {res}C:\Users\XUE XINDONG\Desktop\educ_health\World Development Submissions\part1 educ_health_20191007.smcl
  {txt}log type:  {res}smcl
 {txt}opened on:  {res} 7 Oct 2019, 14:34:02

{com}. do "C:\Users\XUEXIN~1\AppData\Local\Temp\STD3f28_000000.tmp"
{txt}
{com}. use "C:\Users\XUE XINDONG\Desktop\educ_health\meta_educ_health(20190928).dta",
{txt}
{com}. 
. ******************************************************************************
. *DEFINITION OF BASIC VARIABLES
. ******************************************************************************
. gen tstat=tstatistics
{txt}
{com}. gen negative = 1
{txt}
{com}. replace negative = -1 if BadHealth == 1 & BadEduc == 0
{txt}(3,330 real changes made)

{com}. replace negative = -1 if BadHealth == 0 & BadEduc == 1
{txt}(80 real changes made)

{com}. replace tstat = tstat*negative
{txt}(3,397 real changes made)

{com}. gen df = n-k1
{txt}
{com}. gen r = tstat/sqrt(tstat^2+df)
{txt}
{com}. gen varR = (1-r^2)/df
{txt}
{com}. gen seR = sqrt(varR)
{txt}
{com}. gen pcc = r
{txt}
{com}. gen abststat = abs(tstat)
{txt}
{com}. gen abspcc = abs(pcc)
{txt}
{com}. 
. ******************************************************************************
. * Distribution of t-stats and PCCs before kicking out outliers
. ******************************************************************************
. summ tstat, detail

                            {txt}tstat
{hline 61}
      Percentiles      Smallest
 1%    {res}-3.366447            -20
{txt} 5%    {res}-1.705179         -19.75
{txt}10%    {res}-.6745101      -12.80742       {txt}Obs         {res}      4,718
{txt}25%    {res} .5986928            -12       {txt}Sum of Wgt. {res}      4,718

{txt}50%    {res} 1.959998                      {txt}Mean          {res} 3.053543
                        {txt}Largest       Std. Dev.     {res} 6.912549
{txt}75%    {res} 3.290574            104
{txt}90%    {res}        7            109       {txt}Variance      {res} 47.78333
{txt}95%    {res} 11.94444            125       {txt}Skewness      {res} 7.950496
{txt}99%    {res}     27.7       137.5762       {txt}Kurtosis      {res} 105.9487
{txt}
{com}. summ pcc, detail

                             {txt}pcc
{hline 61}
      Percentiles      Smallest
 1%    {res}-.0611414      -.1993045
{txt} 5%    {res} -.018811      -.1753322
{txt}10%    {res}-.0071823      -.1448239       {txt}Obs         {res}      4,718
{txt}25%    {res}  .003816      -.1299282       {txt}Sum of Wgt. {res}      4,718

{txt}50%    {res} .0150329                      {txt}Mean          {res} .0311541
                        {txt}Largest       Std. Dev.     {res} .0625404
{txt}75%    {res} .0438438        .854579
{txt}90%    {res} .0878176       .9144696       {txt}Variance      {res} .0039113
{txt}95%    {res} .1214882       .9151465       {txt}Skewness      {res} 5.387409
{txt}99%    {res} .2419359       .9489114       {txt}Kurtosis      {res} 57.61067
{txt}
{com}. summ df, detail 

                             {txt}df
{hline 61}
      Percentiles      Smallest
 1%    {res}      250             12
{txt} 5%    {res}      612             47
{txt}10%    {res}      985             47       {txt}Obs         {res}      4,718
{txt}25%    {res}     3372             48       {txt}Sum of Wgt. {res}      4,718

{txt}50%    {res}     9349                      {txt}Mean          {res} 67163.41
                        {txt}Largest       Std. Dev.     {res} 290314.3
{txt}75%    {res}    26442        3781410
{txt}90%    {res}   106691        3781410       {txt}Variance      {res} 8.43e+10
{txt}95%    {res}   205749        3781410       {txt}Skewness      {res} 8.915053
{txt}99%    {res}  1823897        3781410       {txt}Kurtosis      {res} 92.25565
{txt}
{com}. 
. 
. ******************************************************************************
. *Kicking out outliers
. ******************************************************************************
. reg pcc seR

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}     4,718
{txt}{hline 13}{c +}{hline 34}   F(1, 4716)      = {res}   307.04
{txt}       Model {c |} {res} 1.12776086         1  1.12776086   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 17.3218199     4,716   .00367299   {txt}R-squared       ={res}    0.0611
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0609
{txt}       Total {c |} {res} 18.4495808     4,717  .003911295   {txt}Root MSE        =   {res} .06061

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}         pcc{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}seR {c |}{col 14}{res}{space 2} 1.143686{col 26}{space 2} .0652692{col 37}{space 1}   17.52{col 46}{space 3}0.000{col 54}{space 4} 1.015728{col 67}{space 3} 1.271645
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .0149453{col 26}{space 2} .0012783{col 37}{space 1}   11.69{col 46}{space 3}0.000{col 54}{space 4} .0124391{col 67}{space 3} .0174514
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. predict resid, residual
{txt}
{com}. egen stdr=std(resid)
{txt}
{com}. gen absr=abs(stdr)
{txt}
{com}. drop if absr>3.5
{txt}(47 observations deleted)

{com}. drop resid stdr absr
{txt}
{com}. 
. 
. ******************************************************************************
. * Characteristics of t-stats and PCCs of final data set
. ******************************************************************************
. summ tstat, detail

                            {txt}tstat
{hline 61}
      Percentiles      Smallest
 1%    {res}-3.366447            -20
{txt} 5%    {res}-1.705179         -19.75
{txt}10%    {res}-.6745101      -12.80742       {txt}Obs         {res}      4,671
{txt}25%    {res} .5871212            -12       {txt}Sum of Wgt. {res}      4,671

{txt}50%    {res} 1.952008                      {txt}Mean          {res} 2.843497
                        {txt}Largest       Std. Dev.     {res} 6.097447
{txt}75%    {res} 3.285714       90.87731
{txt}90%    {res}      6.5            109       {txt}Variance      {res} 37.17885
{txt}95%    {res}       11            125       {txt}Skewness      {res} 8.438453
{txt}99%    {res}       25       137.5762       {txt}Kurtosis      {res} 134.6089
{txt}
{com}. histogram tstat,bin(80)
{txt}(bin={res}80{txt}, start={res}-20{txt}, width={res}1.9697023{txt})
{res}{txt}
{com}. 
. histogram tstat, bin(25) name(histTSTA1)
{txt}(bin={res}25{txt}, start={res}-20{txt}, width={res}6.3030473{txt})
{res}{txt}
{com}. gen tstat1 = (tstat<-2)
{txt}
{com}. gen tstat2 = (tstat>=-2 & tstat<=2)
{txt}
{com}. gen tstat3 = (tstat>2)
{txt}
{com}. 
. ge tstatn=0
{txt}
{com}. replace tstatn=1 if tstat1==1
{txt}(169 real changes made)

{com}. replace tstatn=2 if tstat2==1
{txt}(2,319 real changes made)

{com}. replace tstatn=3  if tstat3==1
{txt}(2,183 real changes made)

{com}. ta tstatn

     {txt}tstatn {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}        169        3.62        3.62
{txt}          2 {c |}{res}      2,319       49.65       53.26
{txt}          3 {c |}{res}      2,183       46.74      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      4,671      100.00
{txt}
{com}. 
. // NOTE: A lot of insignificant t-stats
. sum tstat1 tstat2 tstat3

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 6}tstat1 {c |}{res}      4,671    .0361807    .1867595          0          1
{txt}{space 6}tstat2 {c |}{res}      4,671    .4964676    .5000411          0          1
{txt}{space 6}tstat3 {c |}{res}      4,671    .4673517    .4989864          0          1
{txt}
{com}. 
. 
. 
. summ pcc, detail

                             {txt}pcc
{hline 61}
      Percentiles      Smallest
 1%    {res} -.060682      -.1448239
{txt} 5%    {res}-.0187959      -.1284819
{txt}10%    {res}-.0071823      -.1268878       {txt}Obs         {res}      4,671
{txt}25%    {res} .0036618      -.1195891       {txt}Sum of Wgt. {res}      4,671

{txt}50%    {res} .0147812                      {txt}Mean          {res} .0274673
                        {txt}Largest       Std. Dev.     {res} .0437326
{txt}75%    {res} .0422708       .2567372
{txt}90%    {res} .0841243       .2583877       {txt}Variance      {res} .0019125
{txt}95%    {res} .1118175       .2658183       {txt}Skewness      {res} 1.459769
{txt}99%    {res} .1842374       .2751812       {txt}Kurtosis      {res} 7.257037
{txt}
{com}. histogram pcc, bin(80) name(histPCC)
{txt}(bin={res}80{txt}, start={res}-.1448239{txt}, width={res}.00525006{txt})
{res}{txt}
{com}. 
. histogram pcc if PhysicalHealth1==1, bin(80) name(histPHPCC)
{txt}(bin={res}80{txt}, start={res}-.1448239{txt}, width={res}.00525006{txt})
{res}{txt}
{com}. histogram pcc if Mentalhealth1==1, bin(80) name(histMHPCC)
{txt}(bin={res}80{txt}, start={res}-.12848193{txt}, width={res}.00462618{txt})
{res}{txt}
{com}. histogram pcc if Generalhealth1==1, bin(80) name(histGHPCC)
{txt}(bin={res}80{txt}, start={res}-.11958908{txt}, width={res}.00439654{txt})
{res}{txt}
{com}. 
. sum PhysicalHealth1 Mentalhealth1 Generalhealth1

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
PhysicalHe~1 {c |}{res}      4,671    .7458788    .4354127          0          1
{txt}Mentalheal~1 {c |}{res}      4,671    .0760009    .2650279          0          1
{txt}Generalhea~1 {c |}{res}      4,671    .1781203     .382655          0          1
{txt}
{com}. 
. ******************************************************************************
. * Relationship between t-stats and PCCs
. ******************************************************************************
. // This gives the correlation between pcc and tstat. It's not real high (0.61).
. // Next I graph the two to show the positive correlation.
. corr pcc tstat
{txt}(obs=4,671)

             {c |}      pcc    tstat
{hline 13}{c +}{hline 18}
         pcc {c |}{res}   1.0000
       {txt}tstat {c |}{res}   0.5039   1.0000

{txt}
{com}. graph twoway (scatter pcc tstat, msize(*0.5) msymbol(Oh) name(PCCtstat)) (lfit pcc tstat) 
{res}{txt}
{com}. summ df, detail

                             {txt}df
{hline 61}
      Percentiles      Smallest
 1%    {res}      339             66
{txt} 5%    {res}      616            130
{txt}10%    {res}      993            130       {txt}Obs         {res}      4,671
{txt}25%    {res}     3378            130       {txt}Sum of Wgt. {res}      4,671

{txt}50%    {res}     9408                      {txt}Mean          {res} 67786.29
                        {txt}Largest       Std. Dev.     {res} 291702.3
{txt}75%    {res}    28138        3781410
{txt}90%    {res}   106691        3781410       {txt}Variance      {res} 8.51e+10
{txt}95%    {res}   205749        3781410       {txt}Skewness      {res}  8.87056
{txt}99%    {res}  1823897        3781410       {txt}Kurtosis      {res} 91.34764
{txt}
{com}. 
. ******************************************************************************
. * Relationship of PCCs over time
. ******************************************************************************
. 
. bysort ID: egen PCCmed = median(pcc)
{txt}
{com}. graph twoway (scatter PCCmed PubYear, msize(*0.5) msymbol(Oh) name(PCCtime21)) (lfit PCCmed PubYear) 
{res}{txt}
{com}. 
. 
. ******************************************************************************
. **CALCULATING THE MEAN EFFECT WITHOUT CORRECTING FOR PUBLICATION BIAS
. ******************************************************************************
. 
. 
. ******************************************************************************
. * Calculating study weights based on number of estimates per study
. ******************************************************************************
. bysort ID: egen numberests = count(ID)
{txt}
{com}. gen weight = 1/numberests
{txt}
{com}. 
. 
. ******************************************************************************
. * FIXED EFFECTS
. ******************************************************************************
. 
. gen feprecisionR = 1/seR
{txt}
{com}. gen fetstatR = r/seR
{txt}
{com}. 
. 
. ******************************************************************************
. * Equal weight to each estimate (Column 1)
. ******************************************************************************
. regress fetstatR feprecisionR, noc vce(cluster ID)

{txt}Linear regression                               Number of obs     = {res}     4,671
                                                {txt}F(1, 104)         =  {res}    11.74
                                                {txt}Prob > F          = {res}    0.0009
                                                {txt}R-squared         = {res}    0.2008
                                                {txt}Root MSE          =    {res} 6.0148

{txt}{ralign 78:(Std. Err. adjusted for {res:105} clusters in ID)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}    fetstatR{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
feprecisionR {c |}{col 14}{res}{space 2} .0115741{col 26}{space 2} .0033777{col 37}{space 1}    3.43{col 46}{space 3}0.001{col 54}{space 4} .0048759{col 67}{space 3} .0182722
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. 
. ******************************************************************************
. * Equal weight to each study (Column (2)
. ******************************************************************************
. regress fetstatR feprecisionR [pweight = weight],  noc vce(cluster ID)
{txt}(sum of wgt is 105)

Linear regression                               Number of obs     = {res}     4,671
                                                {txt}F(1, 104)         =  {res}     5.43
                                                {txt}Prob > F          = {res}    0.0217
                                                {txt}R-squared         = {res}    0.1839
                                                {txt}Root MSE          =    {res} 11.231

{txt}{ralign 78:(Std. Err. adjusted for {res:105} clusters in ID)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}    fetstatR{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
feprecisionR {c |}{col 14}{res}{space 2} .0135791{col 26}{space 2} .0058269{col 37}{space 1}    2.33{col 46}{space 3}0.022{col 54}{space 4} .0020242{col 67}{space 3}  .025134
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. 
. ******************************************************************************
. * RANDOM EFFECTS 
. ******************************************************************************
. //metan r seR, random
. metareg r seR , wsse(seR)

{txt}Meta-regression{col 55}Number of obs{col 70}= {res}   4671
REML{txt} estimate of between-study variance{col 55}tau2{col 70}={res} .001194
{txt}% residual variation due to heterogeneity{col 55}I-squared_res{col 70}= {res} 97.11%
{txt}Proportion of between-study variance explained{col 55}Adj R-squared {col 70}= {res}  8.06%
With{txt} Knapp-Hartung modification
{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}           r{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}seR {c |}{col 14}{res}{space 2} 1.130735{col 26}{space 2} .0609901{col 37}{space 1}   18.54{col 46}{space 3}0.000{col 54}{space 4} 1.011166{col 67}{space 3} 1.250305
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .0121513{col 26}{space 2} .0008938{col 37}{space 1}   13.59{col 46}{space 3}0.000{col 54}{space 4}  .010399{col 67}{space 3} .0139036
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. scalar tau2 =  e(tau2)
{txt}
{com}. gen revarR = varR + tau2
{txt}
{com}. gen reseR = sqrt(revarR)
{txt}
{com}. 
. 
. ******************************************************************************
. * Correcting for heteroskedasticity
. ******************************************************************************
. gen reprecisionR = 1/reseR
{txt}
{com}. gen retstatR = r/reseR
{txt}
{com}. gen repubbiasR = seR/reseR
{txt}
{com}. 
. 
. ******************************************************************************
. * Equal weight to each estimate (Column 3)
. ******************************************************************************
. regress retstatR reprecisionR ,  noc vce(cluster ID)

{txt}Linear regression                               Number of obs     = {res}     4,671
                                                {txt}F(1, 104)         =  {res}    94.76
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.2774
                                                {txt}Root MSE          =    {res} 1.0727

{txt}{ralign 78:(Std. Err. adjusted for {res:105} clusters in ID)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}    retstatR{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
reprecisionR {c |}{col 14}{res}{space 2} .0249379{col 26}{space 2} .0025618{col 37}{space 1}    9.73{col 46}{space 3}0.000{col 54}{space 4} .0198578{col 67}{space 3}  .030018
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. 
. ******************************************************************************
. * Equal weight to each study (Column 4)
. ******************************************************************************
. regress retstatR  reprecisionR [pweight = weight] , noc vce(cluster ID)
{txt}(sum of wgt is 105)

Linear regression                               Number of obs     = {res}     4,671
                                                {txt}F(1, 104)         =  {res}   120.46
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.3025
                                                {txt}Root MSE          =    {res}  1.225

{txt}{ralign 78:(Std. Err. adjusted for {res:105} clusters in ID)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}    retstatR{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
reprecisionR {c |}{col 14}{res}{space 2} .0304678{col 26}{space 2}  .002776{col 37}{space 1}   10.98{col 46}{space 3}0.000{col 54}{space 4} .0249628{col 67}{space 3} .0359727
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. 
. 
. ******************************************************************************
. * DETECTING PUBLICATION BIAS
. ******************************************************************************
. 
. ******************************************************************************
. * Funnel Plot for individual estimates
. ******************************************************************************
. // FUNNEL GRAPH
. metafunnel r seR, ylabel(#8) name(funnelA) //name(funnel, replace) nolines

{txt}Note: default data input format (theta, se_theta) assumed.
{res}{txt}
{com}. metafunnel r seR if seR < 0.4, ylabel(#8) name(funnelAA) //name(funnel, replace) nolines

{txt}Note: default data input format (theta, se_theta) assumed.
{res}{txt}
{com}. //graph save funnel1, replace
. //NOTE: No evidence of asymmetry in estimates, hence no evidence of publication bias
. //NOTE: But much evidence of random effects!
. 
. ******************************************************************************
. * Funnel Plot for study means
. ******************************************************************************
. //FUNNEL GRAPH
. by ID, sort: egen meanr = mean(r)
{txt}
{com}. by ID, sort: egen meanseR = mean(seR)
{txt}
{com}. metafunnel meanr meanseR, ylabel(#8) name(funnelB)  //name(funnel, replace) nolines

{txt}Note: default data input format (theta, se_theta) assumed.
{res}{txt}
{com}. 
. 
. ******************************************************************************
. * FAT/PET
. ******************************************************************************
. 
. ******************************************************************************
. * FIXED EFFECTS
. ******************************************************************************
. 
. ******************************************************************************
. * Equal weight to each estimate (Column 1)
. ******************************************************************************
. regress fetstatR feprecisionR,  vce(cluster ID)

{txt}Linear regression                               Number of obs     = {res}     4,671
                                                {txt}F(1, 104)         =  {res}     3.30
                                                {txt}Prob > F          = {res}    0.0723
                                                {txt}R-squared         = {res}    0.0701
                                                {txt}Root MSE          =    {res} 5.8804

{txt}{ralign 78:(Std. Err. adjusted for {res:105} clusters in ID)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}    fetstatR{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
feprecisionR {c |}{col 14}{res}{space 2} .0078257{col 26}{space 2}   .00431{col 37}{space 1}    1.82{col 46}{space 3}0.072{col 54}{space 4}-.0007212{col 67}{space 3} .0163725
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 1.599568{col 26}{space 2} .5811931{col 37}{space 1}    2.75{col 46}{space 3}0.007{col 54}{space 4} .4470405{col 67}{space 3} 2.752096
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. 
. ******************************************************************************
. * Equal weight to each study (Column (2)
. ******************************************************************************
. regress fetstatR feprecisionR  [pweight = weight],  vce(cluster ID)
{txt}(sum of wgt is 105)

Linear regression                               Number of obs     = {res}     4,671
                                                {txt}F(1, 104)         =  {res}     2.57
                                                {txt}Prob > F          = {res}    0.1117
                                                {txt}R-squared         = {res}    0.0939
                                                {txt}Root MSE          =    {res} 11.114

{txt}{ralign 78:(Std. Err. adjusted for {res:105} clusters in ID)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}    fetstatR{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
feprecisionR {c |}{col 14}{res}{space 2} .0108753{col 26}{space 2} .0067793{col 37}{space 1}    1.60{col 46}{space 3}0.112{col 54}{space 4}-.0025683{col 67}{space 3} .0243189
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 1.942669{col 26}{space 2} .8397449{col 37}{space 1}    2.31{col 46}{space 3}0.023{col 54}{space 4}  .277423{col 67}{space 3} 3.607914
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. 
. ******************************************************************************
. * RANDOM EFFECTS 
. ******************************************************************************
. 
. ******************************************************************************
. * Equal weight to each estimate (Column 3)
. ******************************************************************************
. regress retstatR  reprecisionR repubbiasR,  noc vce(cluster ID)

{txt}Linear regression                               Number of obs     = {res}     4,671
                                                {txt}F(2, 104)         =  {res}    56.25
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.3269
                                                {txt}Root MSE          =    {res} 1.0354

{txt}{ralign 78:(Std. Err. adjusted for {res:105} clusters in ID)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}    retstatR{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
reprecisionR {c |}{col 14}{res}{space 2} .0121513{col 26}{space 2} .0030232{col 37}{space 1}    4.02{col 46}{space 3}0.000{col 54}{space 4} .0061562{col 67}{space 3} .0181464
{txt}{space 2}repubbiasR {c |}{col 14}{res}{space 2} 1.130735{col 26}{space 2} .2643983{col 37}{space 1}    4.28{col 46}{space 3}0.000{col 54}{space 4} .6064234{col 67}{space 3} 1.655047
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. 
. ******************************************************************************
. * Equal weight to each study (Column 4)
. ******************************************************************************
. regress retstatR  reprecisionR   repubbiasR [pweight = weight] , noc vce(cluster ID)
{txt}(sum of wgt is 105)

Linear regression                               Number of obs     = {res}     4,671
                                                {txt}F(2, 104)         =  {res}    73.79
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.3705
                                                {txt}Root MSE          =    {res}  1.164

{txt}{ralign 78:(Std. Err. adjusted for {res:105} clusters in ID)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}    retstatR{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
reprecisionR {c |}{col 14}{res}{space 2} .0146639{col 26}{space 2} .0036052{col 37}{space 1}    4.07{col 46}{space 3}0.000{col 54}{space 4} .0075146{col 67}{space 3} .0218133
{txt}{space 2}repubbiasR {c |}{col 14}{res}{space 2} 1.402851{col 26}{space 2} .3118958{col 37}{space 1}    4.50{col 46}{space 3}0.000{col 54}{space 4} .7843503{col 67}{space 3} 2.021353
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. 
. 
. ******************************************************************************
. * *PEESE
. ******************************************************************************
. 
. ******************************************************************************
. * FIXED EFFECTS
. ******************************************************************************
. 
. ******************************************************************************
. * Equal weight to each estimate (Column 1)
. ******************************************************************************
. regress fetstatR feprecisionR seR,  noc vce(cluster ID)

{txt}Linear regression                               Number of obs     = {res}     4,671
                                                {txt}F(2, 104)         =  {res}    33.68
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.2177
                                                {txt}Root MSE          =    {res} 5.9512

{txt}{ralign 78:(Std. Err. adjusted for {res:105} clusters in ID)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}    fetstatR{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
feprecisionR {c |}{col 14}{res}{space 2} .0108621{col 26}{space 2} .0034327{col 37}{space 1}    3.16{col 46}{space 3}0.002{col 54}{space 4}  .004055{col 67}{space 3} .0176692
{txt}{space 9}seR {c |}{col 14}{res}{space 2} 48.29333{col 26}{space 2} 13.76235{col 37}{space 1}    3.51{col 46}{space 3}0.001{col 54}{space 4} 21.00207{col 67}{space 3}  75.5846
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. 
. ******************************************************************************
. * Equal weight to each study (Column (2)
. ******************************************************************************
. regress fetstatR feprecisionR seR [pweight = weight], noc vce(cluster ID)
{txt}(sum of wgt is 105)

Linear regression                               Number of obs     = {res}     4,671
                                                {txt}F(2, 104)         =  {res}    44.37
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.1914
                                                {txt}Root MSE          =    {res} 11.181

{txt}{ralign 78:(Std. Err. adjusted for {res:105} clusters in ID)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}    fetstatR{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
feprecisionR {c |}{col 14}{res}{space 2} .0132341{col 26}{space 2}  .005892{col 37}{space 1}    2.25{col 46}{space 3}0.027{col 54}{space 4} .0015501{col 67}{space 3} .0249182
{txt}{space 9}seR {c |}{col 14}{res}{space 2} 53.17937{col 26}{space 2} 15.50729{col 37}{space 1}    3.43{col 46}{space 3}0.001{col 54}{space 4} 22.42784{col 67}{space 3}  83.9309
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. 
. ******************************************************************************
. * RANDOM EFFECTS 
. ******************************************************************************
. ge  repubbiasRR = varR/reseR
{txt}
{com}. 
. ******************************************************************************
. * Equal weight to each estimate (Column 3)
. ******************************************************************************
. regress retstatR  reprecisionR   repubbiasRR,  noc vce(cluster ID)

{txt}Linear regression                               Number of obs     = {res}     4,671
                                                {txt}F(2, 104)         =  {res}    54.43
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.3085
                                                {txt}Root MSE          =    {res} 1.0495

{txt}{ralign 78:(Std. Err. adjusted for {res:105} clusters in ID)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}    retstatR{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
reprecisionR {c |}{col 14}{res}{space 2} .0207287{col 26}{space 2} .0025894{col 37}{space 1}    8.01{col 46}{space 3}0.000{col 54}{space 4} .0155938{col 67}{space 3} .0258637
{txt}{space 1}repubbiasRR {c |}{col 14}{res}{space 2}  19.5979{col 26}{space 2} 7.069693{col 37}{space 1}    2.77{col 46}{space 3}0.007{col 54}{space 4} 5.578431{col 67}{space 3} 33.61736
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. 
. ******************************************************************************
. * Equal weight to each study (Column 4)
. ******************************************************************************
. regress retstatR  reprecisionR repubbiasRR  [pweight = weight] , noc vce(cluster ID)
{txt}(sum of wgt is 105)

Linear regression                               Number of obs     = {res}     4,671
                                                {txt}F(2, 104)         =  {res}    70.65
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.3591
                                                {txt}Root MSE          =    {res} 1.1745

{txt}{ralign 78:(Std. Err. adjusted for {res:105} clusters in ID)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}    retstatR{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
reprecisionR {c |}{col 14}{res}{space 2} .0243955{col 26}{space 2} .0027818{col 37}{space 1}    8.77{col 46}{space 3}0.000{col 54}{space 4} .0188792{col 67}{space 3} .0299118
{txt}{space 1}repubbiasRR {c |}{col 14}{res}{space 2} 26.07548{col 26}{space 2} 6.864446{col 37}{space 1}    3.80{col 46}{space 3}0.000{col 54}{space 4} 12.46302{col 67}{space 3} 39.68793
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. 
. //log close
.  
. 
{txt}end of do-file

{com}. do "C:\Users\XUEXIN~1\AppData\Local\Temp\STD3f28_000000.tmp"
{txt}
{com}. clear
{txt}
{com}. set more off
{txt}
{com}. set type double
{txt}
{com}. graph drop _all
{txt}
{com}. program drop _all
{txt}
{com}. set scheme s2mono
{txt}
{com}. 
{txt}end of do-file

{com}. log close
      {txt}name:  {res}<unnamed>
       {txt}log:  {res}C:\Users\XUE XINDONG\Desktop\educ_health\World Development Submissions\part1 educ_health_20191007.smcl
  {txt}log type:  {res}smcl
 {txt}closed on:  {res} 7 Oct 2019, 14:37:45
{txt}{.-}
{smcl}
{txt}{sf}{ul off}